Securing & Red-Teaming Generative AI Deployments has been added to your cart.
Date: Oct 21, 2026 – Oct 23, 2026   Location:
 

Securing & Red-Teaming Generative AI Deployments

Secure generative AI deployments against threats that don’t fit traditional cybersecurity playbooks. GenAI security foundations cover the distinctions from traditional cybersecurity, the CIA triad...

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$3,000USD
Duration 3 days
Course Code GAI-2501
Available Formats Classroom, Virtual
Next Class: Oct 21, 2026

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Overview

Course Description

Secure generative AI deployments against threats that don’t fit traditional cybersecurity playbooks. GenAI security foundations cover the distinctions from traditional cybersecurity, the CIA triad applied to generative AI, security by modality, and current threat actors. Threat taxonomies cover the OWASP Top 10 for LLMs across prompt injection, supply-chain vulnerabilities, data and model poisoning, sensitive disclosure, vector and embedding weaknesses, misinformation, excessive agency, and unbounded consumption, alongside the MITRE ATLAS kill-chain phases from reconnaissance through impact. Red-teaming foundations and automation cover assembling a red team, open-ended versus closed-list testing, recording results, custom benchmarks, PyRIT, and DeepEval-driven security benchmarks. Securing training data, mitigating input and output attacks, protecting models, and maintaining security posture cover access control, sanitization, provenance, integrity, model hardening, guardrails, spotlighting, output obfuscation, AI Bill of Materials, watermarking, and the NIST AI RMF applied to ongoing GenAI security. Hands-on labs produce a poisoned dataset, an indirect prompt-injection exploit, an excessive-agency exploit, a PyRIT benchmark, a sanitized data pipeline, and an AI Bill of Materials. The course is designed for cybersecurity professionals, DevOps engineers, software developers, and data scientists with practical cybersecurity experience.

Skills Gained

By the end of this course, participants will be able to:

  • Secure generative AI deployments against unique threat patterns
  • Identify and mitigate vulnerabilities using the OWASP Top 10 for LLMs and MITRE ATLAS
  • Implement red-teaming techniques to uncover security gaps
  • Apply strategies to protect models, data, and inference pipelines
  • Establish long-term security posture using the NIST AI RMF

Who Can Benefit

This course is designed for:

  • Cybersecurity
  • DevOps
  • Software Developers
  • Data Scientists

Prerequisites

Participants should enter this course with:

  • Comfort writing Python code and working in developer interfaces
  • Practical cybersecurity experience and/or a major cybersecurity certification (e.g., Security+, CISSP)
  • Foundational knowledge of generative AI, prompt engineering, RAG, and GenAI development patterns (covered by GAI-1101 or similar)

Organizational Objectives

This course assists organizations to:

  • Reduce GenAI breach risk through systematic OWASP and ATLAS-based threat modeling
  • Lower remediation cost by red-teaming before deployment rather than after incidents
  • Establish auditable security telemetry and AI-BOM provenance for AI workloads
  • Build a working AI-security vocabulary across security and development teams

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Introduction to Generative AI Security

By the end of this module, you will be able to articulate how GenAI security differs from traditional cybersecurity, apply the CIA triad to generative AI, recognize modality-specific security concerns, and profile threat actors targeting GenAI.

  • What differentiates GenAI security from traditional cybersecurity?
  • Applying the CIA Triad to Generative AI
  • Security by Modality
  • Current Threat Actors
  • Hands-on Lab: Apply CIA-triad brainstorming to a sample GenAI system and profile one threat actor from a real case study.

OWASP Top 10 for LLM

By the end of this module, you will be able to recognize the OWASP Top 10 for LLMs — prompt injection, supply chain risks, data and model poisoning, sensitive disclosure, vector and embedding weaknesses, misinformation, excessive agency, and unbounded consumption.

  • Prompt Injection and Indirect Prompt Injection
  • Supply Chain Vulnerabilities and Data & Model Poisoning
  • Sensitive Information Disclosure, Improper Output Handling, and System Prompt Leakage
  • Vector and Embedding Weaknesses
  • Misinformation, Excessive Agency, and Unbounded Consumption
  • Hands-on Lab: Poison a dataset and detect the resulting misinformation in a downstream LLM.

MITRE ATLAS Tactics and Techniques

By the end of this module, you will be able to map MITRE ATLAS tactics across the kill chain — reconnaissance, initial access, ML model access, privilege escalation, defense evasion, discovery, ML attack staging, exfiltration, and impact.

  • Reconnaissance, Resource Development, and Initial Access
  • ML Model Access, Execution, and Persistence
  • Privilege Escalation, Defense Evasion, and Credential Access
  • Discovery and Collection
  • ML Attack Staging, Exfiltration, and Impact
  • Hands-on Lab: Execute an indirect prompt-injection attack and exploit excessive agency in a sample agent.

Foundations of GenAI Red-Teaming

By the end of this module, you will be able to assemble a red-team group, decide what to test, choose between open-ended and closed-list testing, and capture the testing output.

  • Building a Group of Red Teamers
  • Deciding What to Test
  • Open-Ended vs. Closed-List Testing
  • Recording the Testing Output
  • Hands-on Lab: Run a red-teaming strategy session and red-team a local LLM end-to-end.

Automating GenAI Red-Teaming

By the end of this module, you will be able to distinguish adversarial simulations from model evaluations, apply security-and-harm benchmarks, and build custom benchmarks for a specific deployment.

  • Adversarial Simulations vs. Model Evaluations
  • Benchmarks for Security and Harm
  • Building Custom Benchmarks
  • Hands-on Lab: Benchmark an LLM with DeepEval and build a custom security benchmark with PyRIT.

Securing Training Data

By the end of this module, you will be able to control access to training data, sanitize textual datasets, maintain dataset provenance, and verify dataset integrity through hashes.

  • Controlling Access to Training Data
  • Sanitizing Training Data
  • Maintaining Dataset Provenance
  • Verifying Datasets Integrity
  • Hands-on Lab: Sanitize one textual dataset and verify integrity through hashes against the original.

Mitigating Input and Output Data Attacks

By the end of this module, you will be able to harden models with guardrails, detect and recover from adversarial inputs, apply active and passive output obfuscation, restrict queries, and use ensemble methods to reduce attack surface.

  • Model Hardening & Guardrails
  • Detecting and Recovering Adversarial Inputs
  • Active and Passive Output Obfuscation
  • Restricting Queries and Using Ensemble Methods
  • Hands-on Lab: Apply spotlighting to reduce prompt injections and obfuscate output for one production-style LLM.

Protecting Models

By the end of this module, you will be able to limit public release of model information, control access to model artifacts, verify ML artifact integrity, build an AI Bill of Materials, and instrument AI monitoring and telemetry logging.

  • Limiting Public Release of Information
  • Controlling Access to Model Artifacts
  • Verifying ML Artifacts
  • AI Bill of Materials
  • AI Monitoring and Telemetry Logging
  • Hands-on Lab: Understand LLM watermarking and create an AI Bill of Materials for one production-style model.

Using the NIST AI Risk Management Framework to Maintain Generative AI Security

By the end of this module, you will be able to apply the NIST AI RMF core functions to GenAI, use the Generative AI Profile, integrate security into governance, and maintain the security posture over time.

  • Understanding the Core Functions
  • The Generative AI Profile
  • Applying the NIST AI RMF to GenAI
  • Integrating Security into Governance
  • Maintaining Security Posture
  • Hands-on Lab: Map security controls to the NIST AI RMF core functions and draft a GenAI security risk strategy.

Schedule

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Easy to work with. Learning material pdfs were able to be printed out in color which was very nice to write on.

Exit certified was great as it is very in depth and hands on learning which made it very easy to learn this type of work.

ExitCertified gave me some good trainings and I got to learn through doing labs.

Instructor, Training material & span of the training is neatly planned.

my experince was great from the day i regetered to the actuall day of the class.